Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Methods of Medium Optimization

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

Root-Locus Method

Root-Locus Method

A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block diagram,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Corrigendum to 'Role of electroencephalogram oscillations and the spectrogram in monitoring anaesthesia' [BJA Education 20 (2020) 166-172].

BJA education·2021

Same author

Role of electroencephalogram oscillations and the spectrogram in monitoring anaesthesia.

BJA education·2021

Same author

Using EEG markers to make inferences about anaesthetic-induced altered states of arousal.

British journal of anaesthesia·2018

Same author

Electroencephalographic markers of brain development during sevoflurane anaesthesia in children up to 3 years old.

British journal of anaesthesia·2018

Same author

On compression and damage evolution in two thermoplastics.

Proceedings. Mathematical, physical, and engineering sciences·2017

Same author

The Ageing Brain: Age-dependent changes in the electroencephalogram during propofol and sevoflurane general anaesthesia.

British journal of anaesthesia·2015

Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

See all related articles

Search research articles

Related Experiment Video

Updated: May 14, 2026

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

A fast iterative greedy algorithm for MEG source localization.

G Obregon-Henao¹, B Babadi, C Lamus

¹Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, USA. obregon@nmr.mgh.harvard.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|February 1, 2013

Summary

This summary is machine-generated.

This study introduces a fast greedy algorithm for Magnetoencephalography (MEG) source localization, significantly reducing computational complexity. The new method improves localization accuracy compared to traditional approaches.

More Related Videos

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy

Published on: September 20, 2024

Related Experiment Videos

Last Updated: May 14, 2026

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy

Published on: September 20, 2024

Area of Science:

Neuroscience
Biomedical Engineering
Computational Science

Background:

Dynamic source localization algorithms for Magnetoencephalography (MEG) inverse problems enhance estimation quality using cortical spatio-temporal dynamics.
Current methods face high computational complexity due to the large number of sources requiring estimation.

Purpose of the Study:

To introduce a computationally efficient algorithm for sparse source localization in MEG.
To improve the accuracy of MEG source localization while minimizing computational cost.

Main Methods:

Developed a fast iterative greedy algorithm incorporating subspace pursuit for sparse source localization.
Employed a reduced-order state-space model to achieve significant computational savings.
Conducted simulation studies on MEG data.

Main Results:

The proposed algorithm demonstrated substantial gains in localization accuracy compared to the minimum-norm estimate.
Achieved these accuracy improvements with a negligible increase in computational complexity.
Significant computational savings were realized through the use of a reduced-order state-space model.

Conclusions:

The fast iterative greedy algorithm offers a computationally efficient and accurate solution for MEG source localization.
This method presents a viable alternative to existing algorithms, particularly for complex dynamic source estimations.
The approach effectively balances accuracy and computational load in neuroimaging analysis.